www.pudn.com > HMM1.zip > logist2Fit.m


function [beta, p] = logist2Fit(y, x, addOne, w)
> LOGIST2FIT 2 class logsitic classification
> function beta = logist2Fit(y,x, addOne)
>
> y(i) = 0/1
> x(:,i) = i'th input - we optionally append 1s to last dimension
> w(i) = optional weight
>
> beta(j)- regression coefficient

if nargin < 3, addOne = 1; end
if nargin < 4, w = 1; end

Ncases = size(x,2);
if Ncases ~= length(y)
error(sprintf('size of data = >dx>d, size of labels=>d', size(x,1), size(x,2), length(y)))
end
if addOne
x = [x; ones(1,Ncases)];
end
[beta, p] = logist2(y(:), x', w(:));
beta = beta(:);